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Patterns of consent: evidence from a general household survey

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  • Stephen P. Jenkins
  • Lorenzo Cappellari
  • Peter Lynn
  • Annette Jäckle
  • Emanuela Sala

Abstract

Summary. We analyse patterns of consent and consent bias in the context of a large general household survey, the ‘Improving survey measurement of income and employment’ survey, also addressing issues that arise when there are multiple consent questions. A multivariate probit regression model for four binary outcomes with two incidental truncations is used. We show that there are biases in consent to data linkage with benefit and tax credit administrative records that are held by the Department for Work and Pensions, and with wage and employment data held by employers. There are also biases in respondents’ willingness and ability to supply their national insurance number. The biases differ according to the question that is considered. We also show that modelling questions on consent independently rather than jointly may lead to misleading inferences about consent bias. A positive correlation between unobservable individual factors affecting consent to Department for Work and Pensions record linkage and consent to employer record linkage is suggestive of a latent individual consent propensity.

Suggested Citation

  • Stephen P. Jenkins & Lorenzo Cappellari & Peter Lynn & Annette Jäckle & Emanuela Sala, 2006. "Patterns of consent: evidence from a general household survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 701-722, October.
  • Handle: RePEc:bla:jorssa:v:169:y:2006:i:4:p:701-722
    DOI: 10.1111/j.1467-985X.2006.00417.x
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    References listed on IDEAS

    as
    1. Lynn, Peter & Jäckle, Annette & Jenkins, Stephen P. & Sala, Emanuela, 2004. "The effects of dependent interviewing on responses to questions on income sources," ISER Working Paper Series 2004-16, Institute for Social and Economic Research.
    2. Lorenzo Cappellari & Stephen P. Jenkins, 2004. "Modelling low income transitions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 593-610.
    3. Stewart, Mark B & Swaffield, Joanna K, 1999. "Low Pay Dynamics and Transition Probabilities," Economica, London School of Economics and Political Science, vol. 66(261), pages 23-42, February.
    4. F. Thomas Juster & Richard Suzman, 1995. "An Overview of the Health and Retirement Study," Journal of Human Resources, University of Wisconsin Press, vol. 30, pages 7-56.
    5. Van de Ven, Wynand P. M. M. & Van Praag, Bernard M. S., 1981. "The demand for deductibles in private health insurance : A probit model with sample selection," Journal of Econometrics, Elsevier, vol. 17(2), pages 229-252, November.
    6. Philip J. Smith & David C. Hoaglin & J. N. K. Rao & Michael P. Battaglia & Danni Daniels, 2004. "Evaluation of adjustments for partial non‐response bias in the US National Immunization Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(1), pages 141-156, February.
    7. Jäckle, Annette & Sala, Emanuela & Jenkins, Stephen P. & Lynn, Peter, 2004. "Validation of survey data on income and employment: the ISMIE experience," ISER Working Paper Series 2004-14, Institute for Social and Economic Research.
    8. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    9. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, September.
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    11. Jenkins, Stephen P. & Lynn, Peter & Jäckle, Annette & Sala, Emanuela, 2004. "Linking household survey and administrative record data: what should the matching variables be?," ISER Working Paper Series 2004-23, Institute for Social and Economic Research.
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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